Bibliographic record
Abstract
Existing research has shown that disability is costly and can result in an increased risk of living in poverty and a decrease in living standards. In this paper, we expand a framework of equality budgeting, previously applied from a gender perspective, to the population of households affected by disability. Using a microsimulation model linked to data from the EU Survey of Income and Living Conditions (EU-SILC), we show how tax-benefit policy and other market income changes between 2007 and 2019 impacted households affected by disability and households not affected by disability. We find that disposable (or post-tax and transfer) income grew for both types of households but at a faster rate for households affected by disability than households not affected by disability. This income growth was driven by two counteracting forces. On the one hand, tax and welfare policy failed to keep pace with market income growth, reducing the living standards of households affected by disability by more than households not affected by disability. On the other hand, despite having lower average wage levels, wage growth for workers affected by disability outpaced wage growth for workers not affected by disability, while the labour supply of households affected by disability also increased. Future attempts to equality-proof budgetary policy should consider that changes to welfare disproportionally affect households with disabilities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".